"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
from copy import deepcopy

import torch
import torch.nn as nn
from functools import partial

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_
from .registry import register_model


__all__ = ['Cait', 'ClassAttn', 'LayerScaleBlockClassAttn', 'LayerScaleBlock', 'TalkingHeadAttn']


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 384, 384), 'pool_size': None,
        'crop_pct': 1.0, 'interpolation': 'bicubic', 'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed.proj', 'classifier': 'head',
        **kwargs
    }


default_cfgs = dict(
    cait_xxs24_224=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/XXS24_224.pth',
        input_size=(3, 224, 224),
    ),
    cait_xxs24_384=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/XXS24_384.pth',
    ),
    cait_xxs36_224=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/XXS36_224.pth',
        input_size=(3, 224, 224),
    ),
    cait_xxs36_384=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/XXS36_384.pth',
    ),
    cait_xs24_384=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/XS24_384.pth',
    ),
    cait_s24_224=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/S24_224.pth',
        input_size=(3, 224, 224),
    ),
    cait_s24_384=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/S24_384.pth',
    ),
    cait_s36_384=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/S36_384.pth',
    ),
    cait_m36_384=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/M36_384.pth',
    ),
    cait_m48_448=_cfg(
        url='https://dl.fbaipublicfiles.com/deit/M48_448.pth',
        input_size=(3, 448, 448),
    ),
)


class ClassAttn(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to do CA 
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.k = nn.Linear(dim, dim, bias=qkv_bias)
        self.v = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        q = self.q(x[:, 0]).unsqueeze(1).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        q = q * self.scale
        v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        attn = (q @ k.transpose(-2, -1))
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
        x_cls = self.proj(x_cls)
        x_cls = self.proj_drop(x_cls)

        return x_cls


class LayerScaleBlockClassAttn(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to add CA and LayerScale
    def __init__(
            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
            drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=ClassAttn,
            mlp_block=Mlp, init_values=1e-4):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = attn_block(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
        self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
        self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)

    def forward(self, x, x_cls):
        u = torch.cat((x_cls, x), dim=1)
        x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
        x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
        return x_cls


class TalkingHeadAttn(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf)
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
        super().__init__()

        self.num_heads = num_heads

        head_dim = dim // num_heads

        self.scale = head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)

        self.proj = nn.Linear(dim, dim)

        self.proj_l = nn.Linear(num_heads, num_heads)
        self.proj_w = nn.Linear(num_heads, num_heads)

        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]

        attn = (q @ k.transpose(-2, -1))

        attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)

        attn = attn.softmax(dim=-1)

        attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class LayerScaleBlock(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to add layerScale
    def __init__(
            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
            drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=TalkingHeadAttn,
            mlp_block=Mlp, init_values=1e-4):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = attn_block(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
        self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
        self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)

    def forward(self, x):
        x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
        x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
        return x


class Cait(nn.Module):
    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    # with slight modifications to adapt to our cait models
    def __init__(
            self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
            num_heads=12, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0.,
            drop_path_rate=0.,
            norm_layer=partial(nn.LayerNorm, eps=1e-6),
            global_pool=None,
            block_layers=LayerScaleBlock,
            block_layers_token=LayerScaleBlockClassAttn,
            patch_layer=PatchEmbed,
            act_layer=nn.GELU,
            attn_block=TalkingHeadAttn,
            mlp_block=Mlp,
            init_scale=1e-4,
            attn_block_token_only=ClassAttn,
            mlp_block_token_only=Mlp,
            depth_token_only=2,
            mlp_ratio_clstk=4.0
    ):
        super().__init__()

        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim

        self.patch_embed = patch_layer(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)

        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [drop_path_rate for i in range(depth)]
        self.blocks = nn.ModuleList([
            block_layers(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                act_layer=act_layer, attn_block=attn_block, mlp_block=mlp_block, init_values=init_scale)
            for i in range(depth)])

        self.blocks_token_only = nn.ModuleList([
            block_layers_token(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio_clstk, qkv_bias=qkv_bias,
                drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=norm_layer,
                act_layer=act_layer, attn_block=attn_block_token_only,
                mlp_block=mlp_block_token_only, init_values=init_scale)
            for i in range(depth_token_only)])

        self.norm = norm_layer(embed_dim)

        self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        trunc_normal_(self.pos_embed, std=.02)
        trunc_normal_(self.cls_token, std=.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)

        x = x + self.pos_embed
        x = self.pos_drop(x)

        for i, blk in enumerate(self.blocks):
            x = blk(x)

        for i, blk in enumerate(self.blocks_token_only):
            cls_tokens = blk(x, cls_tokens)

        x = torch.cat((cls_tokens, x), dim=1)

        x = self.norm(x)
        return x[:, 0]

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x


def checkpoint_filter_fn(state_dict, model=None):
    if 'model' in state_dict:
        state_dict = state_dict['model']
    checkpoint_no_module = {}
    for k, v in state_dict.items():
        checkpoint_no_module[k.replace('module.', '')] = v
    return checkpoint_no_module


def _create_cait(variant, pretrained=False, **kwargs):
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Vision Transformer models.')

    model = build_model_with_cfg(
        Cait, variant, pretrained,
        default_cfg=default_cfgs[variant],
        pretrained_filter_fn=checkpoint_filter_fn,
        **kwargs)
    return model


@register_model
def cait_xxs24_224(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs)
    model = _create_cait('cait_xxs24_224', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_xxs24_384(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=192, depth=24, num_heads=4, init_scale=1e-5, **kwargs)
    model = _create_cait('cait_xxs24_384', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_xxs36_224(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs)
    model = _create_cait('cait_xxs36_224', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_xxs36_384(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=192, depth=36, num_heads=4, init_scale=1e-5, **kwargs)
    model = _create_cait('cait_xxs36_384', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_xs24_384(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=288, depth=24, num_heads=6, init_scale=1e-5, **kwargs)
    model = _create_cait('cait_xs24_384', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_s24_224(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs)
    model = _create_cait('cait_s24_224', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_s24_384(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=384, depth=24, num_heads=8, init_scale=1e-5, **kwargs)
    model = _create_cait('cait_s24_384', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_s36_384(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=384, depth=36, num_heads=8, init_scale=1e-6, **kwargs)
    model = _create_cait('cait_s36_384', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_m36_384(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=768, depth=36, num_heads=16, init_scale=1e-6, **kwargs)
    model = _create_cait('cait_m36_384', pretrained=pretrained, **model_args)
    return model


@register_model
def cait_m48_448(pretrained=False, **kwargs):
    model_args = dict(patch_size=16, embed_dim=768, depth=48, num_heads=16, init_scale=1e-6, **kwargs)
    model = _create_cait('cait_m48_448', pretrained=pretrained, **model_args)
    return model
